Traditional BI solutions claim to connect all your disparate sources, but they don't address the elephant in the room: how can we efficiently query our data sources (including our Big Data sources) without taxing our infrastructure resources?
In-place analytics from Pyramid 2018 allows you to model data where your data lives.
This results in much faster query times and reduced data transmission costs. With Pyramid 2018, you can model on any ANSI SQL-compliant source, Big Data engines (including Apache Drill, Apache Ignite, and Apache HAWQ), Microsoft engines (include SSAS OLAP and tabular), or Pyramid's internal in-memory engine.
In-place modeling lets you model data where the data lives. You have the option to “shard” queries to SQL, Apache/Hadoop engines, ANSI SQL, or to centralized locations like Pyramid Analytics’ high-performing in-memory engine (IMDB) or SQL Analysis Services. This maximizes users’ ability to quickly and accurately access data need when they need it. All of this is made possible by the Pyramid Query Language (PQL). It optimizes your connection to these different data sources—providing the ubiquity of SQL with the analytic power of MDX.
See Pyramid features in action.
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